In a potential annual market of 30 million hearing aids, only 5.5 million instruments are sold. Moreover, one out of five buyers does not wear the hearing aid(s). Apparently, despite rapid advancements in Digital Signal Processor (DSP) technology, user satisfaction rates remain poor for modern industrial hearing aids.
Over the past decade, hearing aid manufacturers have focused on incorporating very advanced DSP technology and algorithms in their hearing aids. As a result, current DSP algorithms for industrial hearing aids feature a few hundred tuning parameters. In order to reduce the complexity of fitting the hearing aid to a specific user, manufacturers leave only a few tuning parameters adjustable and fix the rest to ‘reasonable’ values. Oftentimes, this results in a very sophisticated DSP algorithm that does not satisfactorily match the specific hearing loss characteristics and perceptual preferences of the user.
It is an object to provide a method for automatic adjustment of signal processing parameters in a hearing aid that is capable of incorporating user perception of sound reproduction, such as sound quality over time.
According to some embodiments, the above-mentioned and other objects are fulfilled in a hearing aid with a signal processor for signal processing in accordance with selected values of a set of parameters Θ, by a method of automatic adjustment of a set z of the signal processing parameters Θ, using a set of learning parameters θ of the signal processing parameters Θ, the method comprising the steps of:
extracting signal features u of a signal in the hearing aid,
recording a measure r of an adjustment e made by the user of the hearing aid, modifying z by the equation:z=uθ+r
and
absorbing the user adjustment e in θ by the equation:θN=Φ(u,r)+θP 
wherein
θN is the new values of the learning parameter set θ,
θP is the previous values of the learning parameter set θ, and
Φ is a function of the signal features u and the recorded adjustment measure r.
Φ may be computed by a normalized Least Means Squares algorithm, a recursive Least Means Squares algorithm, a Kalman algorithm, a Kalman smoothing algorithm, or any other algorithm suitable for absorbing user preferences.
In accordance with some embodiments, in a hearing aid with a signal processor for signal processing in accordance with selected values of a set of parameters Θ, a method of automatic adjustment of a set z of the signal processing parameters Θ, using a set of learning parameters θ of the signal processing parameters Θ is provided, wherein the method includes extracting signal features u of a signal in the hearing aid, recording a measure r of an adjustment e made by the user of the hearing aid, modifying z by the equation z=u θ+r, and absorbing the user adjustment e in θ by the equation θN=Φ(u,r)+θP, wherein θN is the new values of the learning parameter set θ, θP is the previous values of the learning parameter set θ, and Φ is a function of the signal features u and the recorded adjustment measure r.
In one embodiment, the signal features constitutes a matrix U, such as a vector u.
It should be noted that the equation z=u θ+r, underlining indicates a set of variables, such as a multi-dimensional variable, for example a two-dimensional or a one-dimensional variable. The equation constitutes a model, preferably a linear model, mapping acoustic features and user correction onto signal processing parameters.
In some embodiments, z is a one-dimensional variable, the signal features constitute a vector u and the measure r of a user adjustment e is absorbed in θ by the equation:
            θ      _        N    =                    μ                              σ            2                    +                                                    u                _                            T                        ⁢                          u              _                                          ⁢                        u          _                T            ⁢              r        _              +                  θ        _            P      
wherein μ is the step size, and subsequently a new recorded measure rN of the user adjustment e is calculated by the equation:rN=rP−uTθP+e
wherein rP is the previous recorded measure. Further, a new value σN of the user inconsistency estimator σ2 is calculated by the equation:σN2=σP2÷γ└rN2−σP2┘
wherein σP is the previous value of the user inconsistency estimator, and
γ is a constant.
z may be a variable g and r may be a variable r, so thatg=uTθ+r. 
Advantageously, the method in a hearing aid according to the present embodiments has a capability of absorbing user preferences changing aver time and/or changes in typical sound environments experienced by the user. The personalization of the hearing aid is performed during normal use of the hearing aid. These advantages are obtained by absorbing user adjustments of the hearing aid in the parameters of the hearing aid processing. Over time, this approach leads to fewer user manipulations during periods of unchanging user preferences. Further, the method in the hearing aid is robust to inconsistent user behaviour.
According to some embodiments, user preferences for algorithm parameters are elicited during normal use in a way that is consistent and coherent and in accordance with theory for reasoning under uncertainty.
According to some embodiments, the hearing aid is capable of learning a complex relationship between desired adjustments of signal processing parameters and corrective user adjustments that are a personal, time-varying, nonlinear, and/or stochastic.
A hearing aid algorithm F(.) is a recipe for processing an input signal x(t) into an output signal y(t)=F(x(t):θ), where θ ε Θ is a vector of tuning parameters such as compression ratio's, attack and release times, filter cut-off frequencies, noise reduction gains etc. The set of all interesting values for θ constitutes the parameter space Θ and the set of all ‘reachable’ algorithms constitutes an algorithm library F(Θ). After a hearing aid algorithm library F(Θ) has been developed, the next challenging step is to find a parameter vector value θ*ε Θ that maximizes user satisfaction.
The method may for example be employed in automatic control of the volume setting, maximal noise reduction, settings relating to the sound environment, etc.
Fitting is the final stage of parameter estimation, usually carried out in a hearing clinic or dispenser's office, where the hearing aid parameters are adjusted to match a specific user. Typically, according to the prior art the audiologist measures the user profile (e.g. audiogram), performs a few listening tests with the user and adjusts some of the tuning parameters (e.g. compression ratio's) accordingly. However, according to some embodiments, the hearing aid is subsequently subjected to an incremental adjustment of signal processor parameters during its normal use that lowers the requirement for manual adjustments.
After a user has left the dispenser's office, the user may fine-tune the hearing aid using a volume-control wheel or a push-button on the hearing aid with a model that learns from user feedback inside the hearing aid. The personalization process continues during normal use. The traditional volume control wheel may be linked to a new adaptive parameter that is a projection of a relevant parameter space. For example, this new parameter, in the following denoted the personalization parameter, could control (1) simple volume, (2) the number of active microphones or (3) a complex trade-off between noise reduction and signal distortion. By turning the ‘personalization wheel’ to preferred settings and absorbing these preferences in the model resident in the hearing aid, it is possible to keep learning and fine-tuning while a user wears the hearing aid device in the field.
The output of an environment classifier may be included in the user adjustments for provision of a method that is capable of distinguishing different user preferences caused by different sound environments. Hereby, signal processing parameters may automatically be adjusted in accordance with the user's perception of the best possible parameter setting for the actual sound environment.
Thus, in one embodiment, the method further comprises the step of classifying the signal features u into a set of predetermined signal classes with respective classification signal features u*, and substitute signal features u with the classification signal features u* of the respective class.